How AI-based hyperautomation enriches traditional BPM

Can AI integrate and transform traditional enterprise business process management (BPM) approaches? What are the real potentials and implications of Intelligent Process Automation on traditional BPM? What impacts could be coming? 

by Nicolas Chignardet, Head of Customer Success at Bonitasoft

Traditional business process management approaches include both methodology and technology. BPM methodology involves process mapping to eliminate redundancies and enable lifecycle management, including automation and maintenance. BPM tools such as process automation platforms and RPA (Robotic Process Automation) are employed to automate manual operations to optimize efficiency.

How does AI contribute to these BPM foundations? Can it give a new dimension to business process automation?

Hyperautomation as a productivity booster

 AI offers advanced analytical capabilities, forecasting, and continuous improvement. It does not revolutionize methodology, but it can provide more power and speed for analysis. On the technical side, AI can already integrate with existing technological components and boost automation of BPM-powered automated business processes. This combination is known as hyperautomation.  

Here are some examples of applications where automation is complemented by the power of AI.

• Intelligent document processing: AI can extract information from documents to classify and tag it. This information can then be used in  applications such as fraud detection (for example, detecting fake ID cards, validating proof of address, verifying tax declarations, identifying retouched photos).
• Sentiment analysis: In customer interactions (conversations, emails, social media), AI can suggest emotions or opinions in the content. This information can then be used to direct the person to the appropriate service (such as customer support or IT help desk), and trigger follow-up actions.
• Process discovery: AI can help to define and even construct processes. Practical applications include observing executed systems and then defining processes based on those examples; or using system logs to help find and define implicit processes.
• IRPA (Intelligent Robotic Process Automation): AI already automates the execution of robotic processes that operate autonomously. With intelligent RPA, AI is directly integrated into the automated processes. IRPA is ideal for chaining tasks based on predictable patterns that do not require complex decision-making.
• Co-design and optimization with Large Language Models: AI can assist in process design using a specific process-oriented LLM to create a process template based on a formulated request. This means not designing something fundamentally new, but to have a process from the outset ready for continuous improvement using the same AI tool. Process design tools are similar to ChatGPT, with datasets providing templates for dashboards and KPIs.

The current “state of the art” in AI is still evolutionary rather than revolutionary. Hyperautomation reduces costs, maximizes productivity, and generates a wealth of data from digitized processes that can subsequently be reused. As we move towards deeper integration of AI  into business processes, we expect it will lead process designers to work with already processed and structured data, rather than raw material, resulting in significantly greater added value. As intelligent process automation moves towards mimicking human thought processes, we may see more tasks that could be entrusted to it, such as managing unstructured data, image recognition, voice recognition, and natural language processing.

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